Telephones and other communications devices are used all around the globe in a variety of conditions, not just quiet office environments. Voice communications can happen in diverse and harsh acoustic conditions, such as automobiles, airports, restaurants, etc. Specifically, the background acoustic noise can vary from stationary noises, such as road noise and engine noise, to non-stationary noises, such as babble and speeding vehicle noise. Mobile communication devices need to reduce these unwanted background acoustic noises in order to improve the quality of voice communication. If the origin of these unwanted background noises and the desired speech are spatially separated, then the device can extract the clean speech from a noisy microphone signal using beamforming.
One manner of processing environmental sounds to reduce background noise is to place more than one microphone on a mobile communications device. Spatial separation algorithms use these microphones to obtain the spatial information that is necessary to extract the clean speech by removing noise sources that are spatially diverse from the speech source. Such algorithms improve the signal-to-noise ratio (SNR) of the noisy signal by exploiting the spatial diversity that exists between the microphones. One such spatial separation algorithm is adaptive beamforming, which adapts to changing noise conditions based on the received data. Adaptive beamformers may achieve higher noise cancellation or interference suppression compared to fixed beamformers. One such adaptive beamformer is a Generalized Sidelobe Canceller (GSC). The fixed beamformer of a GSC forms a microphone beam towards a desired direction, such that only sounds in that direction are captured, and the blocking matrix of the GSC forms a null towards the desired look direction. One example of a GSC is shown in FIG. 1.
FIG. 1 is an example of an adaptive beamformer according to the prior art. An adaptive beamformer 100 includes microphones 102 and 104, for generating signals x1[n] and x2[n], respectively. The signals x1[n] and x2[n] are provided to a fixed beamformer 110 and to a blocking matrix 120. The fixed beamformer 110 produces a signal, a[n], which is a noise reduced version of the desired signal contained within the microphone signals x1[n] and x2[n]. The blocking matrix 120, through operation of an adaptive filter 122, generates a b[n] signal, which is a noise signal. The relationship between the desired signal components that are present in both of the microphones 102 and 104, and thus signals x1[n] and x2[n], is modeled by a linear time-varying system, and this linear model h[n] is estimated using the adaptive filter 122. The reverberation/diffraction effects and the frequency response of the microphone channel can all be subsumed in the impulse response h[n]. Thus, by estimating the parameters of the linear model, the desired signal (e.g., speech) in one of the microphones 102 and 104 and the filtered desired signal from the other microphone are closely matched in magnitude and phase thereby, greatly reducing the desired signal leakage in the signal b[n]. The signal b[n] is processed in adaptive noise canceller 130 to generate signal w[n], which is a signal containing all correlated noise in the signal a[n]. The signal w[n] is subtracted from the signal a[n] in adaptive noise canceller 130 to generate signal y[n], which is a noise reduced version of the desired signal picked up by microphones 102 and 104.
One problem with the conventional beamformer is that the adaptive blocking matrix 120 may unintentionally remove some noise from the signal b[n] causing noise in the signals b[n] and a[n] to become uncorrelated. This uncorrelated noise cannot be removed in the canceller 130. Thus, some of the undesired noise may remain present in the signal y[n] generated in the processing block 130 from the signal b[n]. The noise correlation is lost in the adaptive filter 122. Thus, it would be desirable to modify processing in the adaptive filter 122 of the conventional adaptive beamformer 100 to operate to reduce destruction of noise cancellation within the adaptive filter 122.
Shortcomings mentioned here are only representative and are included simply to highlight that a need exists for improved electrical components, particularly for signal processing employed in consumer-level devices, such as mobile phones. Embodiments described herein address certain shortcomings but not necessarily each and every one described here or known in the art.